Amelogenin Supramolecular Assembly in Nanospheres Defined by a Complex Helix-Coil-PPII Helix 3D-Structure

Tooth enamel, the hardest material in the human body, is formed within a self-assembled matrix consisting mostly of amelogenin proteins. Here we have determined the complete mouse amelogenin structure under physiological conditions and defined interactions between individual domains. NMR spectroscopy revealed four major amelogenin structural motifs, including an N-terminal assembly of four α-helical segments (S9-V19, T21-P33, Y39-W45, V53-Q56), an elongated random coil region interrupted by two 310 helices (∼P60-Q117), an extended proline-rich PPII-helical region (P118-L165), and a charged hydrophilic C-terminus (L165-D180). HSQC experiments demonstrated ipsilateral interactions between terminal domains of individual amelogenin molecules, i.e. N-terminal interactions with corresponding N-termini and C-terminal interactions with corresponding C-termini, while the central random coil domain did not engage in interactions. Our HSQC spectra of the full-length amelogenin central domain region completely overlapped with spectra of the monomeric Amel-M fragment, suggesting that the central amelogenin coil region did not involve in assembly, even in assembled nanospheres. This finding was confirmed by analytical ultracentrifugation experiments. We conclude that under conditions resembling those found in the developing enamel protein matrix, amelogenin molecules form complex 3D-structures with N-terminal α-helix-like segments and C-terminal PPII-helices, which self-assemble through ipsilateral interactions at the N-terminus of the molecule

Pneumocystis cell wall β-glucan stimulates calcium-dependent signaling of IL-8 secretion by human airway epithelial cells

<p>Abstract</p> <p>Background</p> <p>Respiratory failure secondary to alveolar inflammation during <it>Pneumocystis </it>pneumonia is a major cause of death in immunocompromised patients. Neutrophil infiltration in the lung of patients with <it>Pneumocystis </it>infection predicts severity of the infection and death. Several previous studies indicate that airway epithelial cells release the neutrophil chemoattractant proteins, MIP-2 (rodents) and IL-8 (humans), in response to <it>Pneumocystis </it>and purified <it>Pneumocystis </it>cell wall β-glucans (PCBG) through the NF-κB-dependent pathway. However, little is known about the molecular mechanisms that are involved in the activation of airway epithelium cells by PCBG resulting in the secretion of IL-8.</p> <p>Method</p> <p>To address this, we have studied the activation of different calcium-dependent mitogen-activated protein kinases (MAPKs) in 1HAEo^-cells, a human airway epithelial cell line.</p> <p>Results</p> <p>Our data provide evidence that PCBG induces phosphorylation of the MAPKs, ERK, and p38, the activation of NF-κB and the subsequently secretion of IL-8 in a calcium-dependent manner. Further, we evaluated the role of glycosphingolipids as possible receptors for β-glucans in human airway epithelial cells. Preincubation of the cells with D-<it>threo</it>-1-phenyl-2-decanoylamino-3-morpholino-1-propanol (PDMP) a potent inhibitor of the glycosphingolipids synthesis, prior to PCBG stimulation, significantly decreased IL-8 production.</p> <p>Conclusion</p> <p>These data indicate that PCBG activates calcium dependent MAPK signaling resulting in the release of IL-8 in a process that requires glycosphingolipid for optimal signaling.</p

Effect of Fibrin Glue on the Biomechanical Properties of Human Descemet's Membrane

10.1371/journal.pone.0037456PLoS ONE75

To Each According to His Need? Variability in the Responses to Inequity in Nonhuman Primates

While it is well established that humans respond to inequity, it remains unclear the extent to which this behavior occurs in our nonhuman primate relatives. By comparing a variety of species, spanning from New World and Old World monkeys to great apes, scientists can begin to answer questions about how the response to inequity evolved, what the function of this response is, and why and how different contexts shape it. In particular, research across nonhuman primate species suggests that the response is quite variable across species, contexts and individuals. In this paper, we aim to review these differences in an attempt to identify and better understand the patterns that emerge from the existing data with the goal of developing directions for future research. To begin, we address the importance of considering socio-ecological factors in nonhuman primates in order to better understand and predict expected patterns of cooperation and aversion to inequity in different species, following which we provide a detailed analysis of the patterns uncovered by these comparisons. Ultimately, we use this synthesis to propose new ideas for research to better understand this response and, hence, the evolution of our own responses to inequity

Clinically applicable deep learning for diagnosis and referral in retinal disease

The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting